Eye movement artifacts represent a critical issue for quantitative electroencephalography (EEG) analysis and a number of mathematical
approaches have been proposed to reduce their contribution in EEG recordings. The aim of this paper was to objectively and quantitatively
evaluate the performance of ocular filtering methods with respect to spectral target variables widely used in clinical and functional EEG studies.
In particular the following methods were applied: regression analysis and some blind source separation (BSS) techniques based on second-order
statistics (PCA, AMUSE and SOBI) and on higher-order statistics (JADE, INFOMAX and FASTICA). Considering blind source decomposition
methods, a completely automatic procedure of BSS based on logical rules related to spectral and topographical information was proposed in
order to identify the components related to ocular interference. The automatic procedure was applied in different montages of simulated EEG
and electrooculography (EOG) recordings: a full montage with 19 EEG and 2 EOG channels, a reduced one with only 6 EEG leads and a third
one where EOG channels were not available. Time and frequency results in all of them indicated that AMUSE and SOBI algorithms preserved
and recovered more brain activity than the other methods mainly at anterior regions. In the case of full montage: (i) errors were lower than
5% for all spectral variables at anterior sites; and (ii) the highest improvement in the signal-to-artifact (SAR) ratio was obtained up to 40 dB
at these anterior sites. Finally, we concluded that second-order BSS-based algorithms (AMUSE and SOBI)